Online Prediction of Milling Inner Hole Roundness Error Based on Accurate SSEM Value Extraction

Abstract

To improve the machining accuracy and production efficiency of precision components with deep hole structures, an online prediction method of the inner hole roundness error, which cannot be directly measured in real time during the machining process, is proposed in this paper. For online prediction of the workpiece roundness error (WRE) during machining, a predictive model based on correlation analysis and a proportional method is proposed according to the spindle synchronous error motion (SSEM) by three-probe method testing. To improve the prediction accuracy of the WRE, a particle swarm optimization (PSO) algorithm is introduced for optimizing a probe mounting angle of a three-probe method, and a harmonic wavelet method for SSEM feature extraction is proposed. Using the PSO algorithm, the optimal probe mounting angle of the three-probe method is obtained, the influence of spindle surface roundness on SSEM is eliminated, and the higher-order harmonic suppression of the three-probe method is avoided effectively. By the harmonic wavelet method, the accurate SSEM extraction is enhanced and the WRE prediction accuracy is promoted. The experiments show that the inner hole roundness error online prediction method proposed in this paper has high prediction accuracy.